Many studies of neonates with a variety of conditions support EEG as a good prognostic indicator of outcome. However, the availability of expert neonatal EEG is very limited, available only at a relatively few specialized pediatric centers in the country. In addition, long-term monitoring of the EEG, which can provide the most accurate prognosis in neonates, is rarely used in clinical practice now because of the considerable effort required reviewing long-term data. This Phase II project will continue the development of accurate and reliable signal processing methods specific for analyzing and characterizing the background EEG in neonates. The objective is to develop an automated classifier of the background neonatal EEG that will agree with assessments made by clinical experts. The eventual goal is the development of a low-cost, easy-to-use monitor that can provide long-term continuous assessments of the neonatal background EEG. Such a monitor could be a good measure of treatment success, indicating neurological improvement or decline after treatment has been given. Also, it could impact level of care decisions, especially for those neonates given poor prognoses (e.g.: severe neurological deficits, or death).